A software tool for large-scale systematic epigenome imputation. ChromImpute takes an existing compendium of epigenomic data and uses it to predict signal tracks for mark-sample combinations not experimentally mapped or to generate a potentially more robust version of data sets that have been mapped experimentally. ChromImpute bases its predictions on features from signal tracks of other marks that have been mapped in the target sample and the target mark in other samples with these features combined using an ensemble of regression trees.
Computes a similarity metric between two ChIP-seq datasets to quantify chromatin interactions. In contrast to a basic count of overlaps between two Transcription Factor Binding Sites, IntervalStats allows to compute an exact P-value on their similarity metric. This metric is asymmetric and they demonstrate that it can highlight particular behaviour such as "co-factor" function of a protein. For every query interval, this method produces the closest reference interval, the distance between them and P-value. Their method is insensitive to non-biological variation in datasets (peak width for example). Furthermore, IntervalStats similarity computation can be restricted to a set of genomic regions (such as mappable genome, promoters, open chromatin regions). So it can model peak location biases.
Allows users to jointly analyze ChIP-chip and ChIP-seq datasets using hidden Markov model (HHMM). ChIPmeta consists of method designed for combining ChIP-chip and ChIP-seq data, but the HHMM framework is rather general and can be applied to other scenarios where information collected from multiple sources may be integrated. The software can be useful in biomedical research.
Provides a method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. scHMM is based on an expectation-maximization-type procedure to infer hidden states and other model parameters. This software considers inter-sample correlations in the hidden state inference. Its method is flexible and can be extended to higher-order HMMs by adjoining more covariates in the penalized logistic regression model.
Provides a functional canonical correlation analysis approach. fCCAC method can be used (i) to evaluate reproducibility, and flag datasets showing low canonical correlations; (ii) or to investigate covariation between genetic and epigenetic regulations, to infer their potential functional correlations. It can also be used for developing new hypothesis about how changes in transcription factor (TFs), chromatin remodelling enzymes, histone marks, RNA binding protein and epitranscriptome can cooperatively dictate the specification of cell function and identity.
A computational method to study genome-wide transcriptional regulation of repetitive elements. RepEnrich supports analysis for ChIP-seq and RNA-seq for any organism where a reference genome and repetitive element annotation (such as Repeatmasker annotation) is available. RepEnrich also supports custom repetitive element or repeat feature annotation in bed format.
Calculates an unbiased quantitative measure for DNA sequence specificity. MIM method has extended previous work by further accounting for sequence specificity due to accumulation of weak sequence features. The information can be used as a guide to systematically investigate the regulatory mechanisms for a wide variety of biological processes. By analyzing both simulated and real experimental data, it was found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs. The MIM algorithm is implemented in Python and can be freely accessed for download.
Facilitates analysis of a wide range of time course (TC) data in an automated manner. TDCA models changes in sequencing coverage of individual loci within TC ChIP-seq, or conceptually related experiments, as a function of time. Several customizable options are available, such as the ability to tune modeling parameters, include genome specific analyses, and specify normalization constants. The software can be applied to obtain insights that are of potential biological importance.
Allows to simultaneously handle multiple data sets and takes full advantage of the data to improve the analysis. JAMIE can be generalized to analyze multiple ChIP-seq data sets. It is based on a hierarchical mixture model and permits the capture of the correlations among data sets. This tool assumes that protein–DNA binding can only occur within the potential binding regions (PBRs).
Allows users to predict chromatin looping events by exploiting protein binding signals and sequence motif information. sevenC is an R package that supplies a computational method that can be used for performing the association of transcription factors (TFs) binding sites or of enhancers to regulated genes. The application can also be employed for the improvement of functional downstream analysis at genes level.
Aligns monoallelic expression (MAE) of multiple human and mouse cell types. MaGIC is a pipeline which uses pre-existing models to attribute genes as MAE or biallelic. The application can be employed into the production and training of predictive models with a set of chromatin marks or genomic features. It aims to assist users in highlighting the mechanisms of MAE as well as their functions.
Supplies methodology for interpreting dependencies among chromatin factors for controlling gene regulation. AttentiveChrome is a program able to determine the expression of a gene from an input of histone modification signals covering the gene’s neighboring DNA region. This tool includes features for finding interactions among signals of each chromatin mark, and can simultaneously learn complex dependencies among different marks.
Provides automated and domain-agnostic quality control of Big Data. BDQC is a framework that evaluates the structure and content of the files, progressing from identification of file type and structure to modeling of their content. This method was developed to identify "anomalous" files among a large collection of similar files of arbitrary type with as little guidance from the user as possible.
Calculates metrics which assign a level of similarity between ChIP-Seq profiles. similaRpeak implements six pseudometrics specialized in pattern similarity detection to calculate: (1) the ratio between the areas, (2) the difference between the maximal peaks positions, (3) the ratio between the maximal peaks values, (4) the ratio between the intersection area and the total area, (5) the ratio between the intersection area and the total area of two normalized profiles, and (6) the Spearman’s rho statistic between profiles.
Allows users to detect statistical significance interactions in HiChIP data or PLAC-seq data. FitHiChIP provides an application able to generate four types of interactions among fixed size genomic segments coupled to a normalization technique. This program also includes an utility to create paired-end alignment files and can be run on either a computational cluster or a laptop.
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